1. Field of the Invention
The present invention relates to an image processing apparatus, a magnetic resonance imaging apparatus and an image processing method which produce projected image data and parameter image data from three-dimensional volume data acquired for diagnosis in the medical field.
2. Description of the Related Art
MRI (Magnetic resonance imaging) is one image diagnostic method in the medical field. Magnetic resonance imaging is an imaging method which magnetically excites nuclear spins of an object set in a static magnetic field with an RF (radio frequency) signal having the Larmor frequency and reconstructs an image based on an NMR (nuclear magnetic resonance) signal generated due to the excitation.
Diffusion imaging is an imaging technique based on MRI. Diffusion imaging obtains a DWI (diffusion weighted image) emphasizing diffusion effects by which particles like water molecules spread by Brownian motion due to heat. This diffusion imaging attracts attention for its usefulness in early diagnosis of cerebral infarction. Further, diffusion imaging is developing as DTI (Diffusion tensor imaging) of a cerebral nerve field like detection of aeolotropic properties of a nerve fiber or imaging a nerve fiber with using aeolotropic features. In recent years, applicable scope of MRI has been spreading for the whole body area like an application for screening of cancer.
In diffusion imaging, a pulse sequence having an MPG (Motion Probing Gradient) pulse emphasizing attenuation of MR signals by diffusion can be used. The signal intensity S under diffusion can be represented by expression (1).
Note that, on expression (1), b[s/mm2] denotes a gradient magnetic field factor representing a degree of signal attenuation due to diffusion, ADC (Apparent Diffusion Coefficient) denotes a degree of diffusion, S0 denotes signal intensity when the gradient magnetic field factor b=0.
As a simple method for general clinical applications of diffusion imaging, diagnosis is often performed using only one pair of images of DWI imaged with an MPG pulse in one direction and under about b=1000 and a base image with b=0. Further, because it is normal to set TE (echo time)>60 ms, a base image with b=0 becomes a T2W (T2 weighted image) having contrast emphasizing a difference of transverse relaxation time (T2).
However, a DWI is an image obtained by changing contrast of a base image (with b=0) due to diffusion. Accordingly, not only a component which was changed due to diffusion, a component which was changed due to T1 (longitudinal relaxation time) or T2 becomes mixed in DWI. On the other hand, in a T2W corresponding to b=0, it is often the case that a pathic tissue shows a higher signal intensity than surrounding normal tissue. In this case, in a DWI, its a known phenomenon that pathic tissue continues to show higher signal intensity than surrounding normal tissue even if signal intensity declines due to diffusion, so-called T2 shine through occurs.
Further, in the case of applying an MPG pulse in one direction, a DWI becomes an image having contrast which is dependent on directions of a nerve fiber and an MPG pulse application. That is to say, the more a direction of an MPG pulse application and a traveling direction of a nerve fiber are mutually parallel, signal intensity declines due to diffusion.
Accordingly, what is troubling is a doctor's erroneous reading resulted from MPG direction dependency of contrast due to T2 shine through or DWI.
Therefore, technique is needed which images a DWI to which an MPG is spatially and isotropically changed at least in three directions to cover all and a T2W corresponding to b=0 to obtain an ADC image of only a trace ADC serving as a parameter which is not dependent on a coordinate system. Further, according to need, the aforementioned DTI obtains not only a trace ADC but a quantitative image showing a parameter like FA (Fractional Anisotropy) which is a parameter showing aeolotropy of a nerve fiber by using a T2W corresponding to b=0 and a DWI imaged with changing MPG at least in six directions for diagnosis.
Especially, in the case of screening for a cancer by diffusion imaging, creation of a quantitative image from wide-ranging whole volume data like the entire body is needed (see, for example, Takahara T., Imai Y., Yamashita T., Yasuda S., Nasu S., Van Cauteren M., “Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display”, Radiat Med. 2004 Jul-Aug; 22(4):275-82).
Normally, since diffusion in a part having possibility of a cancer is small compared to that in a normal tissue (that is to say, ADC is small), a part having possibility of a cancer shows a high signal intensity compared to that in a normal tissue on a DWI. On the other hand, on screening of a cancer in whole body organ, a great deal of volume data can be obtained. Because of this, image data is compressed by performing MIP (Maximum Intensity Projection) processing to a DWI. Then, it's often the case that compressed image information is displayed and supplied for diagnosis.
Further, in the case where diffusion imaging applies to screening of a cancer in the body trunk, because of a small ADC in fat, there is a fear of false diagnosis that a fat is a cancer if a DWI which is obtained by normal processing is supplied for diagnosis. Then, on screening for a cancer in the body trunk, collection of a DWI is held after reducing a fat signal using fat suppression in advance. In the case of using fat suppression, it's considered that probability of existence of a cancer tissue is small on a part showing a low signal intensity on a DWI and the part can be assumed as a normal part since the T2 value is short and ADC is large in normal tissue except fat.
Further, in the case that a doctor interprets an ADC image created from a DWI, the doctor diagnoses a high signal part on the DWI concerned with the ADC image. There is a tendency that contrast of a cancer and a normal part of a DWI becomes larger compared to that of an ADC image. This is because a DWI has a synergy effect of contrast due to T2.
FIG. 11 is a T2W showing pancreatic cancer and liver metastasis which is a clinical example of trunk diffusion obtained by conventional magnetic resonance imaging. FIG. 12 is a DWI of the pancreatic cancer and liver metastasis shown in FIG. 11 obtained by conventional magnetic resonance imaging. FIG. 13 is an ADC image of the pancreatic cancer and liver metastasis shown in FIG. 11 obtained by conventional magnetic resonance imaging.
According to T2W (b=0) of FIG. 11, it can be confirmed that a cancer part showed by an arrowhead shows a bit higher signal compared to a surrounding normal tissue. This shows that a T2 value on a cancer part is large. Further, on a DWI (b=1000) of FIG. 12, it can be confirmed that a cancer part showed by an arrowhead is imaged with a further higher signal than that on the T2W. On the contrary, it can be confirmed that signal of a cancer part showed by an arrowhead shows a low intensity on an ADC image of FIG. 13. Further, to according to FIG. 12 and FIG. 13, it can be understood that a contrast difference of a cancer part and a normal tissue of a DWI is larger compared to that of an ADC image.
FIG. 14 is a graph showing variations of respective signal intensities in a normal tissue and tumor each depending on a value of a gradient magnetic field factor b in diffusion imaging.
In FIG. 14, the abscissa indicates a gradient magnetic field factor b [s/mm2] and the ordinate indicates signal intensity. The solid line in FIG. 14 shows variation of signal intensity corresponding to a value of gradient magnetic field factor b in normal tissue, and the dotted line shows variation of signal intensity corresponding to a value of gradient magnetic field factor b in a tumor.
FIG. 14 shows that the tumor has a property that a signal intensity on b=0 is large compared to that in a normal tissue and also an attenuation along increase of b is small. Accordingly, a difference in respective signal intensities of the tumor and the normal tissue on a DWI corresponding to b=1000 becomes larger than a difference in respective signal intensities of the tumor and normal tissue on a T2W corresponding to b=0. As a result, from this it can be understood that detection sensitivity of cancer in a DWI is higher than that in a T2W.
However, on an ADC image, a part having a possibility of a cancer shows a low signal intensity and contrast difference between the part having a possibility of a cancer and surrounding normal tissue is small compared to that in a DWI. Accordingly, projection processing to a two-dimensional plane like MIP processing or mIP (minimum intensity projection) processing is not present in an ADC image. Accordingly, a doctor can't interpret an ADC image only by slice in spite of creation of the ADC image. Because of this, consequently, an interpretation of an ADC image is more different.
On the other hand, an ROI (region of interest) may be set to a part having a possibility of a cancer detected as high signal part on a DWI and diagnosis using the DWI is performed numerically. However, there is fear that arbitrariness of a doctor affects setting of the ROI. Further, a doctor can know numerical information on a part having a possibility of a cancer only as an average value in the whole ROI. Because of this, there is a problem that oversight of a cancer is easy to occur.
Under the background like this, the present situation is that diagnosis of whole body organ using an ADC image is not generalized compared to diagnosis of brain in spite of its importance being recognized. Because of this, there is a fear that trouble happens to accumulation of evidence on a cancer diagnosis.
Thus, a problem like this has commonality to a diagnosis image imaged not only by MRI but by various image diagnostic apparatuses. That is to say, a consequence of need to interpret extravagant image information by a doctor, is a fear that trouble occurs to not only diagnosis efficiency and diagnosis effects but also to adoption of the diagnosis method itself. Examples include the case which obtains not only a DWI but a quantitative value of a different type of parameter like ADC or FA on diffusion imaging of entire body of MRI dealing with a large amount of volume data like above-mentioned. Further, there is a problem that generation of extravagant image information causes increased information processing.